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Consideration of Different Variants of Large Margin Learning Vector Quantization

  • In machine learning, Learning Vector Quantization (LVQ) is well known as supervised vector quantization. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [2]. In many tasks of classification, different variants are considered while training a model and a consideration of variants of large margin in LVQ helps to get significant results [20]. Large margin LVQ (LMLVQ) is to maximize the distance between decision hyperplane and data points. In this thesis, a comparison of different variants of Generalized Learning Vector Quantization (GLVQ) and Large margin in LVQ is proposed along with visualization, implementation and experimental results.

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Metadaten
Author:Avinash Maheshwari
Advisor:Thomas Villmann, Marika Kaden
Document Type:Master's Thesis
Language:English
Year of Completion:2021
Granting Institution:Hochschule Mittweida
Release Date:2023/06/08
GND Keyword:Maschinelles Lernen
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
DDC classes:006.31 Maschinelles Lernen
Open Access:Frei zugänglich
Licence (German):License LogoUrheberrechtlich geschützt